Diabetes is one of the most prevalent chronic diseases in Africa and the world at large, impacting millions of the world population each year and exerting a significant financial burden on the continent’s economy. Diabetes is a chronic illness that impairs an individual’s capacity to control blood glucose levels. It can shorten life expectancy and lower quality of life.
Complications of Diabetes include heart diseases, Retinopathy (causing vision loss), Diabetic foot (ultimately leading to amputation), and Nephropathy (kidney diseases). Diabetes cannot be cured, but many individuals can lessen its negative effects by adopting healthy eating habits, exercising, weight loss, and receiving medical care especially if detected early. In public health management and policy making, predictive models for diabetes risk are valuable resources since early diagnosis can result in lifestyle modifications and more successful treatment.
The Center for Disease Control and Prevention (CDC) estimates that 1 in 5 diabetics, and roughly 8 in 10 prediabetics are unaware of their risk. While there are different types of diabetes, type II diabetes is the most common form and its prevalence varies by age, education, income, location, race, and other social determinants of health. However, much of the burden of the disease falls on those of lower socioeconomic status.
This dataset is the output of a Chinese research study conducted in 2016. It includes 1304 samples of patients who tested positive for Type II diabetes out of 4303 samples, with age of the participants ranging from 21 to 99 years old. The dataset was collected according to the indicators and standards of the World Health Organization. For this project, a csv of the dataset available on Kaggle was used.
Develop a predictive model to identify individuals at high risk of developing type II diabetes.
Can survey questions and investigations from the dataset provide accurate predictions of whether an individual has diabetes? What risk factors are most predictive of diabetes risk?
Age Gender BMI SBP (Systolic Blood Pressure) DBP (Diastolic Blood Pressure) FPG (Fasting Plasma Glucose) FFPG (Final Fasting Plasma Glucose) Cholesterol Triglyceride HDL (High-Density Lipoprotein) LDL (Low-Density Lipoprotein) ALT (Alanine Aminotransferase) BUN (Blood urea nitrogen) CCR (Creatinine Clearance) Smoking Status: (1: Current Smoker, 2: Ever Smoked, 3: Never Smoked) Drinking Status: (1: Current Drinker, 2: Ever Drank, 3: Never Drank) Family History of Diabetes: (1: Yes, 0: No) Diabetes
readr - for reading csv file dplyr - for data manipulation Hmisc - for data analysis, manipulation and statistical modeling ggplot2 - for visualization Plotly - for creating interactive and engaging visualization randomForest - Model Building caret - for Model Evaluation pRoc - for analyzing and visualizing the performance of scoring classifiers in binary classification tasks rpart - creating recursive partitioning models and evaluating how much each feature contributes
Loading dataset using the readr library
library(readr)## Warning: package 'readr' was built under R version 4.2.3data <- read_csv("/Users/m1/Desktop/OGUNDIPE/PAU/Notes/Datasets/Diabetes Dataset.csv")## Rows: 4303 Columns: 18
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (18): Age, Gender, BMI, SBP, DBP, FPG, Chol, Tri, HDL, LDL, ALT, BUN, CC...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Inspection of dataset -Finding missing values -Finding outliers -And other inconsistencies
To get an overview of the structure and contents of the dataset.
library(dplyr)## Warning: package 'dplyr' was built under R version 4.2.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionspec(data)## cols(
## Age = col_double(),
## Gender = col_double(),
## BMI = col_double(),
## SBP = col_double(),
## DBP = col_double(),
## FPG = col_double(),
## Chol = col_double(),
## Tri = col_double(),
## HDL = col_double(),
## LDL = col_double(),
## ALT = col_double(),
## BUN = col_double(),
## CCR = col_double(),
## FFPG = col_double(),
## smoking = col_double(),
## drinking = col_double(),
## family_histroy = col_double(),
## Diabetes = col_double()
## )glimpse(data)## Rows: 4,303
## Columns: 18
## $ Age <dbl> 26, 40, 40, 43, 36, 46, 52, 33, 42, 37, 51, 38, 50, 53,…
## $ Gender <dbl> 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 2…
## $ BMI <dbl> 20.10, 17.70, 19.70, 23.10, 26.50, 20.50, 31.70, 22.90,…
## $ SBP <dbl> 119, 97, 85, 111, 130, 88, 129, 129, 109, 128, 109, 95,…
## $ DBP <dbl> 81, 54, 53, 71, 82, 63, 84, 92, 56, 75, 84, 58, 77, 86,…
## $ FPG <dbl> 5.80, 4.60, 5.30, 4.50, 5.54, 5.76, 5.90, 5.17, 5.06, 4…
## $ Chol <dbl> 4.36, 3.70, 5.87, 4.05, 6.69, 4.60, 6.14, 6.02, 4.73, 6…
## $ Tri <dbl> 0.86, 1.02, 1.29, 0.74, 3.49, 1.00, 2.18, 3.90, 1.02, 0…
## $ HDL <dbl> 0.90, 1.50, 1.75, 1.27, 0.91, 1.32, 1.15, 1.09, 1.15, 2…
## $ LDL <dbl> 2.43, 2.04, 3.37, 2.60, 3.64, 2.78, 3.43, 3.12, 2.82, 4…
## $ ALT <dbl> 12.0, 9.2, 10.1, 36.5, 69.3, 15.0, 26.0, 39.6, 11.5, 8.…
## $ BUN <dbl> 5.40, 3.70, 4.10, 4.38, 3.86, 4.19, 4.70, 4.48, 2.98, 5…
## $ CCR <dbl> 63.8, 70.3, 61.1, 73.4, 67.5, 59.0, 79.0, 68.3, 80.2, 6…
## $ FFPG <dbl> 5.40, 4.10, 4.85, 5.30, 5.53, 4.80, 5.48, 5.84, 5.20, 5…
## $ smoking <dbl> 3, 1, 3, 2, 3, 3, 1, 3, 3, 3, 3, 3, 1, 2, 3, 1, 3, 3, 3…
## $ drinking <dbl> 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3…
## $ family_histroy <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Diabetes <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…Using the Hmisc package to describe data to view missing values and get other descriptions.
library(Hmisc)## Warning: package 'Hmisc' was built under R version 4.2.3##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
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## src, summarize## The following objects are masked from 'package:base':
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## format.pval, unitsdescribe(data)## data
##
## 18 Variables 4303 Observations
## --------------------------------------------------------------------------------
## Age
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 70 1 48.09 16.73 28 30
## .25 .50 .75 .90 .95
## 35 46 59 68 74
##
## lowest : 22 23 24 25 26, highest: 87 88 90 91 93
## --------------------------------------------------------------------------------
## Gender
## n missing distinct Info Mean Gmd
## 4303 0 2 0.684 1.352 0.4561
##
## Value 1 2
## Frequency 2790 1513
## Proportion 0.648 0.352
## --------------------------------------------------------------------------------
## BMI
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 275 1 24.12 3.812 18.8 19.8
## .25 .50 .75 .90 .95
## 21.7 24.0 26.3 28.4 29.9
##
## lowest : 15.6 15.9 16.2 16.4 16.5, highest: 37 37.3 38.7 39.5 45.8
## --------------------------------------------------------------------------------
## SBP
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 111 1 123.2 19.48 98.0 102.0
## .25 .50 .75 .90 .95
## 111.0 122.0 134.0 146.0 154.9
##
## lowest : 72 77 83 85 86, highest: 191 197 198 199 200
## --------------------------------------------------------------------------------
## DBP
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 75 0.999 76.36 12.34 60 63
## .25 .50 .75 .90 .95
## 69 76 83 90 95
##
## lowest : 45 47 48 49 50, highest: 116 120 126 131 134
## --------------------------------------------------------------------------------
## FPG
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 361 1 5.226 0.8816 4.01 4.26
## .25 .50 .75 .90 .95
## 4.70 5.14 5.70 6.40 6.69
##
## lowest : 1.78 2.84 2.93 2.95 3 , highest: 6.95 6.96 6.97 6.98 6.99
## --------------------------------------------------------------------------------
## Chol
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 486 1 4.861 1.026 3.50 3.76
## .25 .50 .75 .90 .95
## 4.20 4.79 5.43 6.05 6.45
##
## lowest : 1.65 2.12 2.21 2.42 2.45 , highest: 8.98 9.28 9.56 9.8 11.65
## --------------------------------------------------------------------------------
## Tri
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 478 1 1.588 1.1 0.510 0.602
## .25 .50 .75 .90 .95
## 0.860 1.280 1.940 2.830 3.559
##
## lowest : 0 0.03 0.12 0.18 0.21 , highest: 12.6 13.1 13.26 14.93 32.64
## --------------------------------------------------------------------------------
## HDL
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 188 0.999 1.667 0.8902 0.900 0.990
## .25 .50 .75 .90 .95
## 1.130 1.340 1.610 2.364 4.861
##
## lowest : 0 0.45 0.48 0.52 0.56
## highest: 2.85 2.98 3.29 3.87 4.86075
## --------------------------------------------------------------------------------
## LDL
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 384 0.999 2.976 0.9834 1.790 2.000
## .25 .50 .75 .90 .95
## 2.350 2.820 3.420 4.708 4.861
##
## lowest : 0.54 0.55 0.69 0.88 1 , highest: 5.54 5.58 5.95 6.09 6.27
## --------------------------------------------------------------------------------
## ALT
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 680 1 26.75 18.97 9.30 11.00
## .25 .50 .75 .90 .95
## 14.30 20.50 31.05 48.00 65.00
##
## lowest : 4.5 4.86075 5 5.2 5.8
## highest: 198 245.9 264 279.2 436.2
## --------------------------------------------------------------------------------
## BUN
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 576 1 4.841 1.367 3.021 3.390
## .25 .50 .75 .90 .95
## 3.960 4.760 5.570 6.430 7.078
##
## lowest : 1.38 1.93 1.95 2 2.21 , highest: 11.42 12.79 13.31 14.64 17.73
## --------------------------------------------------------------------------------
## CCR
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 700 1 71.56 18.3 47.3 51.5
## .25 .50 .75 .90 .95
## 60.0 72.0 82.3 91.7 97.0
##
## lowest : 4.86075 35.6 36.5 36.8 37
## highest: 143.1 145 159.6 214.4 307
## --------------------------------------------------------------------------------
## FFPG
## n missing distinct Info Mean Gmd .05 .10
## 4303 0 479 1 5.729 1.294 4.490 4.630
## .25 .50 .75 .90 .95
## 4.900 5.300 6.020 7.366 8.400
##
## lowest : 3.2 3.57 3.9 3.91 3.95, highest: 15.5 15.6 16.2 20.6 29.7
## --------------------------------------------------------------------------------
## smoking
## n missing distinct Info Mean Gmd
## 4303 0 4 0.782 3.006 1.222
##
## Value 1.000000 2.000000 3.000000 4.860753
## Frequency 745 136 2534 888
## Proportion 0.173 0.032 0.589 0.206
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## drinking
## n missing distinct Info Mean Gmd
## 4303 0 4 0.728 3.21 0.9092
##
## Value 1.000000 2.000000 3.000000 4.860753
## Frequency 83 583 2749 888
## Proportion 0.019 0.135 0.639 0.206
##
## For the frequency table, variable is rounded to the nearest 0
## --------------------------------------------------------------------------------
## family_histroy
## n missing distinct Info Sum Mean Gmd
## 4303 0 2 0.173 265 0.06158 0.1156
##
## --------------------------------------------------------------------------------
## Diabetes
## n missing distinct Info Sum Mean Gmd
## 4303 0 2 0.633 1303 0.3028 0.4223
##
## --------------------------------------------------------------------------------This was done subjectively based on clinical features and realistically possible known values based on previous documented patient presentations and texts.
par(mfrow = c(1, 4))
boxplot(data$BMI, main="BMI Boxplot")
boxplot(data$SBP, main="SBP Boxplot")
boxplot(data$DBP, main="DBP Boxplot")
boxplot(data$Chol, main="Chol Boxplot")
data_updated <- data[data$BMI < 50 & data$BMI > 15 & data$SBP < 240 & data$DBP > 50 & data$Chol < 10, ]
par(mfrow = c(1, 4))
boxplot(data_updated$BMI, main="BMI Boxplot")
boxplot(data_updated$SBP, main="SBP Boxplot")
boxplot(data_updated$DBP, main="DBP Boxplot")
boxplot(data_updated$Chol, main="Chol Boxplot")
MAP is the average arterial pressure throughout one cardiac cycle, systole, and diastole. In individuals with diabetes this is significant due to the vascular complications associated with this condition. Diabetes can lead to changes in blood vessels, including arteriosclerosis (hardening of the arteries) and the development of atherosclerosis (formation of plaques within the arterial walls), which can alter blood pressure levels and affect organ perfusion. Monitoring and managing MAP in individuals with diabetes is essential for reducing the risk of cardiovascular complications, preventing target organ damage and managing co-morbid hypertension. Effective blood pressure control, as part of a comprehensive diabetes management plan, can significantly improve outcomes and quality of life for people with diabetes.
BMI Categorization is necessary to classify the BMI feature in the dataset and for ease of description of insights and visualizations.
data_updated$MAP <- with(data_updated, DBP + (1/3) * (SBP - DBP))
data_updated$BMI_Category <- cut(data_updated$BMI,
breaks=c(-Inf, 18.5, 25, 30, Inf),
labels=c("Underweight", "Normal", "Overweight", "Obese"))
table(data_updated$BMI_Category)##
## Underweight Normal Overweight Obese
## 160 2501 1428 204Using the Str function of the dplyr library to have another quick look at the structure of the updated data.
str(data_updated)## tibble [4,293 × 20] (S3: tbl_df/tbl/data.frame)
## $ Age : num [1:4293] 26 40 40 43 36 46 52 33 42 37 ...
## $ Gender : num [1:4293] 1 1 2 1 1 2 1 1 1 2 ...
## $ BMI : num [1:4293] 20.1 17.7 19.7 23.1 26.5 ...
## $ SBP : num [1:4293] 119 97 85 111 130 88 129 129 109 128 ...
## $ DBP : num [1:4293] 81 54 53 71 82 63 84 92 56 75 ...
## $ FPG : num [1:4293] 5.8 4.6 5.3 4.5 5.54 5.76 5.9 5.17 5.06 4.67 ...
## $ Chol : num [1:4293] 4.36 3.7 5.87 4.05 6.69 4.6 6.14 6.02 4.73 6.75 ...
## $ Tri : num [1:4293] 0.86 1.02 1.29 0.74 3.49 1 2.18 3.9 1.02 0.61 ...
## $ HDL : num [1:4293] 0.9 1.5 1.75 1.27 0.91 1.32 1.15 1.09 1.15 2.4 ...
## $ LDL : num [1:4293] 2.43 2.04 3.37 2.6 3.64 2.78 3.43 3.12 2.82 4.25 ...
## $ ALT : num [1:4293] 12 9.2 10.1 36.5 69.3 15 26 39.6 11.5 8.3 ...
## $ BUN : num [1:4293] 5.4 3.7 4.1 4.38 3.86 4.19 4.7 4.48 2.98 5.03 ...
## $ CCR : num [1:4293] 63.8 70.3 61.1 73.4 67.5 59 79 68.3 80.2 62.7 ...
## $ FFPG : num [1:4293] 5.4 4.1 4.85 5.3 5.53 4.8 5.48 5.84 5.2 5.19 ...
## $ smoking : num [1:4293] 3 1 3 2 3 3 1 3 3 3 ...
## $ drinking : num [1:4293] 3 1 3 3 3 3 3 3 3 3 ...
## $ family_histroy: num [1:4293] 0 0 0 0 0 0 0 0 0 0 ...
## $ Diabetes : num [1:4293] 0 0 0 0 0 0 0 0 0 0 ...
## $ MAP : num [1:4293] 93.7 68.3 63.7 84.3 98 ...
## $ BMI_Category : Factor w/ 4 levels "Underweight",..: 2 1 2 2 3 2 4 2 3 2 ...Using ggplot function of the ggplot2 library to create visualisations and get insights from the data.
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.2.3library(plotly)## Warning: package 'plotly' was built under R version 4.2.3##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:Hmisc':
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## subplot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutggplot(data_updated, aes(x = BMI, fill = factor(Diabetes))) +
geom_histogram(binwidth = 1, color = "black") +
scale_fill_manual(values = c("0" = "blue", "1" = "red"), name = "Diabetes") +
ggtitle("BMI Distribution") +
labs(x = "BMI", y = "Frequency") +
theme_minimal()
ggplot(data_updated, aes(x = BMI_Category, fill = factor(Diabetes))) +
geom_bar(position = "dodge", stat = "count") +
labs(title = "BMI Category Count", x = "BMI Category", y = "Count") +
scale_fill_manual(values = c("0" = "blue", "1" = "red")) +
theme_minimal()
ggplot(data_updated, aes(x = Age, fill = factor(Diabetes))) +
geom_histogram(binwidth = 1, color = "black") +
scale_fill_manual(values = c("0" = "blue", "1" = "red"), name = "Diabetes") +
ggtitle("Age Distribution") +
labs(x = "Age", y = "Frequency") +
theme_minimal()
ggplot(data_updated, aes(x = SBP)) +
geom_histogram(binwidth = 10, boundary = 10, fill = "green", color = "black") +
ggtitle("Systolic Blood Pressure (SBP) Distribution") +
labs(x = "SBP Range", y = "Frequency") +
theme_minimal()
ggplot(data_updated, aes(x=DBP)) +
geom_histogram(binwidth=10, boundary = 10, fill="red", color="black") +
ggtitle("Diastolic Blood Pressure (DBP) Distribution")
ggplot(data_updated, aes(x = MAP, fill = factor(Diabetes))) +
geom_histogram(binwidth = 1, boundary = 0, color = "black") +
scale_fill_manual(values = c("0" = "beige", "1" = "red"), name = "Diabetes") +
ggtitle("Mean Arterial Pressure (MAP) Distribution") +
labs(x = "MAP Range", y = "Frequency") +
theme_minimal()
ggplot(data_updated, aes(x = Chol, fill = factor(Diabetes))) +
geom_histogram(binwidth = 1, color = "black") +
scale_fill_manual(values = c("0" = "purple", "1" = "orange"), name = "Diabetes") +
ggtitle("Cholesterol Distribution") +
labs(x = "Cholesterol Range", y = "Frequency") +
theme_minimal()
interactive_plot <- ggplot(data_updated, aes(x=Age, y=BMI, color=factor(Diabetes))) +
geom_point() +
ggtitle("BMI vs. Age by Diabetes Status")
ggplotly(interactive_plot) ggplot(data_updated, aes(x=Age, y=BMI_Category, color=factor(Diabetes))) +
geom_point() +
ggtitle("Diabetes by BMI Class and Age Distribution")
ggplot(data_updated, aes(x = as.factor(Diabetes), y = Age)) +
geom_boxplot(fill = "cyan", color = "black") +
theme_minimal() +
ggtitle("Age Distribution by Diabetes Status")
Diabetes column indicates whether an individual has diabetes ( 0 and 1 ), This column is essentially categorical, despite being represented with numeric codes as seen in the structure. Treating numeric representations of categories, especially for something like a diabetes status indicator, as double-precision floating-point numbers might not be the most appropriate.The specification of the Diabetes column should be treated as a factor (categorical variable). To improve clarity in data and ensure that statistical analyses and ML are correctly performed.This would also help if there is need to balance the dataset.
data_updated$Diabetes <- as.factor(data_updated$Diabetes)
levels(data_updated$Diabetes) <- c("0", "1")Using the view function to have another quick look at the updated data and be sure data is clean and processed before building a model.
View(data_updated)Load the caret, randomForest and pROC packages Split the data into training and testing sets.
The model was created using the randomForest function where the target variable is Diabetes, and all other variables in the dataset were used as dependent variables. It consists of 500 trees, and at each split, three variables were tried.
Predict on test data Evaluate Further Evaluation using other performance metrics
library(caret)## Loading required package: latticelibrary(randomForest)## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margin## The following object is masked from 'package:dplyr':
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## combinelibrary(pROC)## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
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## cov, smooth, var# Split the data into training and testing sets
set.seed(123) # For reproducibility
trainIndex <- createDataPartition(data_updated$Diabetes, p = .7,
list = FALSE,
times = 1)
diabetes_train <- data_updated[trainIndex, ]
diabetes_test <- data_updated[-trainIndex, ]
# Train the Random Forest model
rf_model <- randomForest(Diabetes ~ ., data = diabetes_train, ntree = 500, mtry = 3)
# Print the model summary
print(rf_model)##
## Call:
## randomForest(formula = Diabetes ~ ., data = diabetes_train, ntree = 500, mtry = 3)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 3
##
## OOB estimate of error rate: 4.32%
## Confusion matrix:
## 0 1 class.error
## 0 2056 38 0.01814709
## 1 92 820 0.10087719# Predict on the test data
predictions <- predict(rf_model, diabetes_test)
# Evaluate the model performance
confusionMatrix <- table(diabetes_test$Diabetes, predictions)
print(confusionMatrix)## predictions
## 0 1
## 0 875 22
## 1 36 354# Calculate other performance metrics - Accuracy, Precision, Recall
accuracy <- sum(diag(confusionMatrix)) / sum(confusionMatrix)
precision <- diag(confusionMatrix) / colSums(confusionMatrix)
recall <- diag(confusionMatrix) / rowSums(confusionMatrix)
print(paste("Accuracy:", accuracy))## [1] "Accuracy: 0.954933954933955"print(paste("Precision:", precision))## [1] "Precision: 0.960482985729967" "Precision: 0.941489361702128"print(paste("Recall:", recall))## [1] "Recall: 0.975473801560758" "Recall: 0.907692307692308"#ROC
roc_response <- roc(diabetes_test$Diabetes, as.numeric(predictions))## Setting levels: control = 0, case = 1## Setting direction: controls < casesplot(roc_response)
auc(roc_response)## Area under the curve: 0.9416Check for importance of each feature
library(rpart)## Warning: package 'rpart' was built under R version 4.2.3DRP_model <- rpart(Diabetes ~ ., data = data_updated)
DRP_model$variable.importance## drinking smoking FFPG HDL LDL FPG
## 1085.377738 1085.377738 513.035508 265.063559 218.469221 196.908898
## CCR DBP SBP
## 7.351077 2.460003 2.460003The out-of-bag (OOB) estimate of the error rate is approximately 4.32%. The OOB error is an estimate of the model’s prediction error calculated using the out-of-bag samples. It provides an estimate of how well the model is likely to perform on unseen data. A lower OOB error indicates better predictive performance, with values closer to 0 indicating higher accuracy.
The confusion matrix shows the model’s performance on the training data. It indicates the number of true negatives (TN) - 2056 false positives (FP) - 38 false negatives (FN) - 92 true positives (TP) - 820 Class Error 0 - 0.018 or 1.8%. Class Error 1 - 0.101 or 10.1%
The accuracy of the model on the training data is approximately 95.49%. While Precision for class 0 (No Diabetes) - 96.05% for class 1 (No Diabetes) - 94.15% Recall/Sensitivity Recall for class 0 (No Diabetes) - 97.55% Recall for class 1 (Diabetic) - 90.77% The area under the curve (AUC) is a measure of the model’s ability to distinguish between classes. An AUC value of 0.9416 indicates a good predictive performance of the model.
The analysis also provides variable importance measures for each predictor variable used in the model. The higher the value, the more important the variable is in predicting the outcome. The most important variables are drinking, smoking and Fasting Plasma Glucose
Overall, the random forest model demonstrates strong performance in classifying diabetes based on the provided predictors, with high accuracy, precision, recall, and AUC. Additionally, the variable importance measures provided insights into which features are most influential in predicting diabetes.